<?xml version='1.0'?><rss version="2.0" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:georss="http://www.georss.org/georss" xmlns:atom="http://www.w3.org/2005/Atom" >
<channel>
	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/40792?offset=610</link>
	<atom:link href="https://bioinformaticsonline.com/related/40792?offset=610" rel="self" type="application/rss+xml" />
	<description><![CDATA[]]></description>
	
	
<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/9675/application-scientist-in-strand-lifesciences-bangalore</guid>
  <pubDate>Mon, 07 Apr 2014 08:17:32 -0500</pubDate>
  <link></link>
  <title><![CDATA[Application Scientist in Strand LifeSciences Bangalore]]></title>
  <description><![CDATA[
<p>Job Description<br />We are looking for a motivated application scientist to help evaluate, compare, and develop next generation sequencing (NGS) data analysis methods. The successful candidate should be able to quickly understand the state-of-art computational biology techniques, prototype them and perform benchmarking studies. The candidate must also be comfortable working with people from different disciplines and be able to present data analysis results in a clear and effective manner. The candidate is also expected to interact with customers as needed, write technical reports and publish new methods and/or data analysis findings in public forums.</p>

<p>Candidate Requirements:<br />A PhD in computer science, computational biology, Bioinformatics, or a related field, along with sufficient programming skills for prototyping. Experience with next generation sequencing data analysis is required. Candidates with MS degree but with relevant work experience can also be considered. </p>

<p>To Apply<br />To apply, please send your updated CV and cover letter to Dr. Rohit Gupta (rohit@strandls.com). </p>

<p>Source: http://www.strandls.com/application-scientist</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/11368/metagenomics-role-in-antibiotic-resistance</guid>
	<pubDate>Mon, 02 Jun 2014 08:04:40 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/11368/metagenomics-role-in-antibiotic-resistance</link>
	<title><![CDATA[Metagenomics role in antibiotic resistance]]></title>
	<description><![CDATA[<p>Related latest article:</p>
<p><a href="http://www.nature.com/nature/journal/v509/n7502/pdf/nature13377.pdf">http://www.nature.com/nature/journal/v509/n7502/pdf/nature13377.pdf</a></p><p>Address of the bookmark: <a href="https://www.landesbioscience.com/journals/virulence/2013VIRULENCE0033R2.pdf" rel="nofollow">https://www.landesbioscience.com/journals/virulence/2013VIRULENCE0033R2.pdf</a></p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34396/pore-an-r-package-for-the-visualization-and-analysis-of-nanopore-sequencing-data</guid>
	<pubDate>Thu, 23 Nov 2017 09:55:57 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34396/pore-an-r-package-for-the-visualization-and-analysis-of-nanopore-sequencing-data</link>
	<title><![CDATA[poRe: an R package for the visualization and analysis of nanopore sequencing data]]></title>
	<description><![CDATA[<p><strong>Motivation:</strong>&nbsp;The Oxford Nanopore MinION device represents a unique sequencing technology. As a mobile sequencing device powered by the USB port of a laptop, the MinION has huge potential applications. To enable these applications, the bioinformatics community will need to design and build a suite of tools specifically for MinION data.</p>
<p><strong>Results:</strong>&nbsp;Here we present poRe, a package for R that enables users to manipulate, organize, summarize and visualize MinION nanopore sequencing data. As a package for R, poRe has been tested on Windows, Linux and MacOSX. Crucially, the Windows version allows users to analyse MinION data on the Windows laptop attached to the device.</p>
<p><strong>Availability and implementation:</strong>&nbsp;poRe is released as a package for R at&nbsp;<a href="http://sourceforge.net/projects/rpore/" target="">http://sourceforge.net/projects/rpore/</a>&nbsp;. A tutorial and further information are available at&nbsp;<a href="https://sourceforge.net/p/rpore/wiki/Home/" target="">https://sourceforge.net/p/rpore/wiki/Home/</a></p>
<p><strong>Contact:</strong><a href="mailto:mick.watson@roslin.ed.ac.uk" target="">mick.watson@roslin.ed.ac.uk</a></p><p>Address of the bookmark: <a href="https://academic.oup.com/bioinformatics/article/31/1/114/2365693" rel="nofollow">https://academic.oup.com/bioinformatics/article/31/1/114/2365693</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37512/purecn-copy-number-calling-and-snv-classification-using-targeted-short-read-sequencing</guid>
	<pubDate>Thu, 09 Aug 2018 04:09:37 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37512/purecn-copy-number-calling-and-snv-classification-using-targeted-short-read-sequencing</link>
	<title><![CDATA[PureCN: copy number calling and SNV classification using targeted short read sequencing]]></title>
	<description><![CDATA[<p>This package estimates tumor purity, copy number, and loss of heterozygosity (LOH), and classifies single nucleotide variants (SNVs) by somatic status and clonality. PureCN is designed for targeted short read sequencing data, integrates well with standard somatic variant detection and copy number pipelines, and has support for tumor samples without matching normal samples.</p>
<p>Author: Markus Riester [aut, cre], Angad P. Singh [aut]</p>
<p>Maintainer: Markus Riester &lt;markus.riester at novartis.com&gt;</p>
<div id="bioc_citation_outer">
<p>Citation (from within R, enter&nbsp;<code>citation("PureCN")</code>):</p>
<div id="bioc_citation">
<p>Riester M, Singh A, Brannon A, Yu K, Campbell C, Chiang D, Morrissey M (2016). &ldquo;PureCN: Copy number calling and SNV classification using targeted short read sequencing.&rdquo;&nbsp;<em>Source Code for Biology and Medicine</em>,&nbsp;<strong>11</strong>, 13. doi:&nbsp;<a href="http://doi.org/10.1186/s13029-016-0060-z">10.1186/s13029-016-0060-z</a>.</p>
</div>
</div><p>Address of the bookmark: <a href="http://bioconductor.org/packages/release/bioc/html/PureCN.html" rel="nofollow">http://bioconductor.org/packages/release/bioc/html/PureCN.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37749/d2tools-the-toolbox-for-counting-the-frequency-of-k-tuple-from-sequencing-datasets-and-calculate-the-dissimilarity</guid>
	<pubDate>Thu, 20 Sep 2018 08:38:29 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37749/d2tools-the-toolbox-for-counting-the-frequency-of-k-tuple-from-sequencing-datasets-and-calculate-the-dissimilarity</link>
	<title><![CDATA[d2Tools: The toolbox for counting the frequency of k-tuple from sequencing datasets and calculate the dissimilarity]]></title>
	<description><![CDATA[<p><code>d2Tools</code>&nbsp;are the toolbox for counting the frequency of K-tuple from sequencing datasets and then calculating the pairwise dissimilarity matrix between samples with the&nbsp;<strong>d2-style</strong>(d2/d2<code>*</code>/d2S representing d2/d2Star/d2shepp, respectively) measures. Hao, Dai, Eucliean, Mahattan, and Chebyshev distance measures are also included in d2Tools.</p>
<p>Manual at&nbsp;https://code.google.com/archive/p/d2-tools/wikis/d2ToolMannual.wiki</p><p>Address of the bookmark: <a href="https://code.google.com/archive/p/d2-tools/" rel="nofollow">https://code.google.com/archive/p/d2-tools/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38205/sim3c-read-pair-simulation-of-3c-based-sequencing-methodologies-hic-meta3c-dnase-hic</guid>
	<pubDate>Tue, 13 Nov 2018 07:25:38 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38205/sim3c-read-pair-simulation-of-3c-based-sequencing-methodologies-hic-meta3c-dnase-hic</link>
	<title><![CDATA[sim3C: Read-pair simulation of 3C-based sequencing methodologies (HiC, Meta3C, DNase-HiC)]]></title>
	<description><![CDATA[<p><strong>Required python modules</strong></p>
<ul>
<li>biopython</li>
<li>intervaltree</li>
<li>numpy</li>
<li>scipy</li>
<li>tqdm</li>
<li>PyYAML</li>
</ul><p>Address of the bookmark: <a href="https://github.com/cerebis/sim3C" rel="nofollow">https://github.com/cerebis/sim3C</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38762/katuali-is-a-flexible-consensus-pipeline-implemented-in-snakemake-to-basecall-assemble-and-polish-oxford-nanopore-technologies-sequencing-data</guid>
	<pubDate>Tue, 22 Jan 2019 06:26:55 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38762/katuali-is-a-flexible-consensus-pipeline-implemented-in-snakemake-to-basecall-assemble-and-polish-oxford-nanopore-technologies-sequencing-data</link>
	<title><![CDATA[Katuali is a flexible consensus pipeline implemented in Snakemake to basecall, assemble, and polish Oxford Nanopore Technologies&#039; sequencing data]]></title>
	<description><![CDATA[<ul>
<li>Run a pipeline processing fast5s to a consensus in a single command.</li>
<li>Recommended fixed "standard" and "fast" pipelines.</li>
<li>Interchange basecaller, assembler, and consensus components of the pipelines simply by changing the target filepath.</li>
<li>Seemless distribution of tasks over local or distributed compute.</li>
<li>Highly configurable.</li>
<li>Open source (Mozilla Public License 2.0).</li>
</ul>
<p>Documentation can be found at&nbsp;<a href="https://nanoporetech.github.io/katuali/">https://nanoporetech.github.io/katuali/</a>.</p><p>Address of the bookmark: <a href="https://github.com/nanoporetech/katuali" rel="nofollow">https://github.com/nanoporetech/katuali</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40544/ngs-bits-short-read-sequencing-tools</guid>
	<pubDate>Thu, 16 Jan 2020 23:14:00 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40544/ngs-bits-short-read-sequencing-tools</link>
	<title><![CDATA[ngs-bits - Short-read sequencing tools]]></title>
	<description><![CDATA[<p>Binaries of&nbsp;<em>ngs-bits</em>&nbsp;are available via Bioconda. Alternatively,&nbsp;<em>ngs-bits</em>&nbsp;can be built from sources:</p>
<ul>
<li><span>Binaries</span>&nbsp;for&nbsp;<a href="https://github.com/imgag/ngs-bits/blob/master/doc/install_bioconda.md">Linux/macOS</a></li>
<li>From&nbsp;<span>sources</span>&nbsp;for&nbsp;<a href="https://github.com/imgag/ngs-bits/blob/master/doc/install_unix.md">Linux/macOS</a></li>
<li>From&nbsp;<span>sources</span>&nbsp;for&nbsp;<a href="https://github.com/imgag/ngs-bits/blob/master/doc/install_win.md">Windows</a></li>
</ul><p>Address of the bookmark: <a href="https://github.com/imgag/ngs-bits" rel="nofollow">https://github.com/imgag/ngs-bits</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41730/parliament2-runs-a-combination-of-tools-to-generate-structural-variant-calls-on-whole-genome-sequencing-data</guid>
	<pubDate>Thu, 28 May 2020 21:57:03 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41730/parliament2-runs-a-combination-of-tools-to-generate-structural-variant-calls-on-whole-genome-sequencing-data</link>
	<title><![CDATA[Parliament2: Runs a combination of tools to generate structural variant calls on whole-genome sequencing data]]></title>
	<description><![CDATA[<p>Parliament2 identifies structural variants in a given sample relative to a reference genome. These structural variants cover large deletion events that are called as Deletions of a region, Insertions of a sequence into a region, Duplications of a region, Inversions of a region, or Translocations between two regions in the genome.</p>
<p>Parliament2 runs a combination of tools to generate structural variant calls on whole-genome sequencing data. It can run the following callers: Breakdancer, Breakseq2, CNVnator, Delly2, Manta, and Lumpy. Because of synergies in how the programs use computational resources, these are all run in parallel. Parliament2 will produce the outputs of each of the tools for subsequent investigation.</p><p>Address of the bookmark: <a href="https://github.com/dnanexus/parliament2" rel="nofollow">https://github.com/dnanexus/parliament2</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/44672/libraries-or-management-tools-for-high-throughput-sequencing-data</guid>
	<pubDate>Fri, 04 Oct 2024 02:45:06 -0500</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/44672/libraries-or-management-tools-for-high-throughput-sequencing-data</link>
	<title><![CDATA[Libraries or management tools for high throughput sequencing data]]></title>
	<description><![CDATA[<ul>
<li><a href="http://gatb.inria.fr/"><span>GATB</span></a>&nbsp;Library.&nbsp;The&nbsp;<span>Genome Analysis Toolbox with de-Bruijn graph.&nbsp;</span>A large part of tools developed by the GenScale team are based on this library.<br />These methods enable the analysis of data sets of any size on multi-core desktop computers, including very huge amount of reads data coming from any kind of organisms such as bacteria, plants, animals and even complex samples (<em>e.g.</em>&nbsp;metagenomes). Among them are (the full is available here:&nbsp;<a href="https://gatb.inria.fr/software/">https://gatb.inria.fr/software/</a>):</li>
<li><a href="https://github.com/morispi/LRez"><span>LRez</span></a>: C++ Library and toolkit for the barcode-based management and indexation of linked-read datasets.</li>
</ul><h2>Variant calling and/or genotyping</h2><ul>
<li><a href="https://gatb.inria.fr/software/discosnp/" title="DiscoSNP">DiscoSNP++ and&nbsp;discoSnpRAD</a>: Reference-free small variant discovery (SNPs and indels)</li>
<li><a href="https://gatb.inria.fr/software/mind-the-gap/" title="MindTheGap">MindTheGap</a>: Detection and assembly of large insertion variants</li>
<li><a href="https://gatb.inria.fr/software/takeabreak/" title="TakeABreak">TakeABreak</a>:&nbsp;reference-free inversion discovery tool</li>
<li><a href="https://github.com/llecompte/SVJedi">SVJedi</a>: Structural Variant genotyper with long read data</li>
<li><a href="https://github.com/SandraLouise/SVJedi-graph">SVJedi-graph</a>: Structural Variant genotyper with long read data using a variation graph</li>
</ul><h2>Sequence assembly</h2><ul>
<li><a href="https://github.com/cguyomar/MinYS">MinYS</a>: reference-guided genome assembly in metagenomics data</li>
<li><a href="https://github.com/anne-gcd/MTG-Link">MTG-link</a>: local assembly tool for linked-read data</li>
<li><a href="https://gatb.inria.fr/software/minia/" title="Minia">Minia</a>: De novo short read assembler</li>
<li><a href="https://gatb.inria.fr/de-novo-genome-assembly/">de-novo pipeline</a>:&nbsp;<em>de-novo</em>&nbsp;assembly pipeline (error correction / contigs / scaffolding) for genomes and meta-genomes</li>
<li><a href="https://gatb.inria.fr/software/mapsembler/" title="Mapsembler2">Mapsembler2</a>: Targeted assembly (not maintained)</li>
</ul><h2>Managing k-mers &amp; indexation</h2><ul>
<li><a href="https://github.com/lrobidou/findere">findere</a>:&nbsp;simple strategy for speeding up queries and for reducing false positive calls from any Approximate Membership Query data structure.
<ul>
<li><a href="https://github.com/lrobidou/fimpera">fimpera</a>&nbsp;extends findere adding the abundance information.</li>
</ul>
</li>
<li><a href="https://github.com/tlemane/kmtricks">kmtricks</a>:&nbsp;modular tool suite for counting kmers, and constructing Bloom filters or kmer matrices, for large collections of sequencing data.</li>
<li><a href="https://github.com/tlemane/kmindex">kmindex&nbsp;</a>is a tool for indexing and querying sequencing samples. It is built on top of kmtricks.</li>
<li><a href="https://github.com/pierrepeterlongo/back_to_sequences">back to sequences</a>: Find sequences (reads, unitigs, genes) related to a set of kmers in large datasets, in a matter of seconds.</li>
<li><a href="https://github.com/vicLeva/bqf">Backpack Quotient Filter</a>:&nbsp;k-mer indexing data structure with abundance</li>
<li><a href="http://github.com/GATB/rconnector">short read connector</a>:&nbsp;Detect similar reads from potentially large read set</li>
<li><a href="https://gatb.inria.fr/software/dsk/" title="DSK">DSK</a>:&nbsp;Count K-mer in sequences</li>
</ul><h2>Pangenome graph manipulation</h2><ul>
<li><a href="https://github.com/Tharos-ux/pancat">Pancat</a>: Pangenome Comparison and Analysis Toolkit</li>
<li><a href="https://pypi.org/project/gfagraphs/">GFAGraphs</a>: a Python library to handle pangenome graph files in GFA format.</li>
</ul><h2>Comparative metagenomics with k-mers</h2><ul>
<li><a href="https://github.com/GATB/simka">Simka and SimkaMin</a>:&nbsp;Comparative metagenomics for large-scale datasets</li>
<li><a href="https://team.inria.fr/genscale/high-throughput-sequence-analysis/compreads-metagenomic-data-analysis/">Comparead &amp; Commet</a>:&nbsp;comparison of metagenomic datasets</li>
</ul><h2>Species and bacterial strains identification</h2><ul>
<li><a href="https://github.com/gsiekaniec/ORI">ORI</a>: software using long nanopore reads to identify bacteria present in a sample at the strain level</li>
<li><a href="https://github.com/kevsilva/StrainFLAIR">StrainFLAIR</a>:&nbsp;STRAIN-level proFiLing using vArIation gRaph</li>
</ul><h2>General-purpose sequencing data manipulation</h2><ul>
<li><a href="https://team.inria.fr/genscale/ngs-software/gassst/">GASSST</a>:&nbsp;long read mapper</li>
<li><a href="https://gatb.inria.fr/software/leon/" title="Leon">Leon</a>: short read compressor (now included in GATB-core)</li>
<li><a href="https://gatb.inria.fr/software/bloocoo/" title="Bloocoo">Bloocoo</a>:&nbsp;short read corrector</li>
<li><a href="https://github.com/GATB/bcalm">BCALM</a>:&nbsp;Construct compacted de Bruijn graphs (unitigs)</li>
</ul><h2>&nbsp;Protein Structure</h2><ul>
<li><a href="https://team.inria.fr/genscale/protein-structure/a-purva-contact-map-overlap-solver/">A_Purva</a>:&nbsp;Contact Map Overlap solver</li>
<li><a href="https://team.inria.fr/genscale/protein-structure/md-jeep-distance-geomtry-solver/">MD-Jeep</a>:&nbsp;Distance Geometry solver</li>
<li><a href="https://team.inria.fr/genscale/csa-comparative-structural-alignment/">CSA</a>:&nbsp;Comparative Structural Alignment</li>
</ul><h2>Workflow</h2><ul>
<li><a href="https://team.inria.fr/genscale/workflows/slicee/">SLICEE</a>:&nbsp;parallel execution of bioinformatics workflows</li>
</ul><h3>Comparative Genomics</h3><ul>
<li><a href="https://team.inria.fr/genscale/comparative-genomics/cassis/">CASSIS</a>:&nbsp;detection of rearrangement breakpoints</li>
<li><a href="https://team.inria.fr/genscale/high-throughput-sequence-analysis/plast-intensive-sequence-comparison/">PLAST</a>:&nbsp;intensive bank-to-bank sequence comparison</li>
<li><a href="https://github.com/stephanierobin/DrjBreakpointFinder">DRJBreakpointFinder</a>: detection and precise localization of excision sites in proviral segments</li>
</ul>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>

</channel>
</rss>